In camera-based image processing systems, such as video conferencing systems, video surveillance and monitoring systems, and human-machine interfaces, it is important to provide a proper calibration for the camera or cameras of the system. For example, in a video conferencing system, it is often desirable to frame the head and shoulders of a particular conference participant in the resultant output video signal, while in a video surveillance system, it may be desirable to frame the entire body of, e.g., a person entering or leaving a restricted area monitored by the system. Accurate performance of such detection and tracking operations generally requires that the cameras involved be properly calibrated.
The calibration process for a given camera may include both an internal calibration and an external calibration. The internal calibration involves camera parameters such as principal point, focal length, and mapping from zoom ticks to focal length. The external calibration involves a determination of the position and orientation of the camera, and may include estimates of pan and tilt biases.
Conventional calibration techniques suffer from a number of significant drawbacks. For example, such techniques are often computationally expensive and may not provide a desired level of stability.
A number of camera-based image processing systems exist which incorporate a graphical user interface (GUI). However, such systems generally utilize the GUI for purposes unrelated to camera calibration. The calibration in such systems is generally performed using one of more of the above-noted conventional techniques, without utilization of the GUI.
It is apparent from the foregoing that a need exists for improved techniques for camera calibration in camera-based image processing systems.